A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Joint stochastic short-term production scheduling and fleet management optimization for mining complexes
https://orcid.org/http://orcid.org/0000-0001-8710-4504
https://orcid.org/http://orcid.org/0000-0001-7329-2209
This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.
Joint stochastic short-term production scheduling and fleet management optimization for mining complexes
https://orcid.org/http://orcid.org/0000-0001-8710-4504
https://orcid.org/http://orcid.org/0000-0001-7329-2209
This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.
Joint stochastic short-term production scheduling and fleet management optimization for mining complexes
https://orcid.org/http://orcid.org/0000-0001-8710-4504
https://orcid.org/http://orcid.org/0000-0001-7329-2209
This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.
Joint stochastic short-term production scheduling and fleet management optimization for mining complexes
Optim Eng
Both, Christian (author) / Dimitrakopoulos, Roussos (author)
Optimization and Engineering ; 21 ; 1717-1743
2020-12-01
27 pages
Article (Journal)
Electronic Resource
English
Short-term mine planning , Production scheduling , Fleet management , Stochastic mixed integer programming , Metaheuristics Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operations Research/Decision Theory , Financial Engineering , Mathematics and Statistics
| Springer Verlag | 2020
A simulation–optimization framework for short-term underground mine production scheduling
| Springer Verlag | 2020
Stochastic freight flow patterns: Implications for fleet optimization
| Online Contents | 1999